Amazon-Web-Services MLS-C01 Free Practice Questions

NEW QUESTION 1
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

• A. The training channel identifying the location of training data on an Amazon S3 bucket.
• B. The validation channel identifying the location of validation data on an Amazon S3 bucket.
• C. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
• D. Hyperparameters in a JSON array as documented for the algorithm used.
• E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
• F. The output path specifying where on an Amazon S3 bucket the trained model will persist.

Answer: AEF

NEW QUESTION 2
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

• A. 1
• B. 4
• C. 7
• D. 10

Answer: C

NEW QUESTION 3
A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

• A. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
• B. AWS Glue with a custom ETL script to transform the data.
• C. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
• D. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

Answer: A

NEW QUESTION 4
A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent
How should the Specialist frame this business problem'?

• A. Streaming classification
• B. Binary classification
• C. Multi-category classification
• D. Regression classification

Answer: A

NEW QUESTION 5
A Machine Learning Specialist is building a supervised model that will evaluate customers' satisfaction with their mobile phone service based on recent usage The model's output should infer whether or not a customer is likely to switch to a competitor in the next 30 days
Which of the following modeling techniques should the Specialist use1?

• A. Time-series prediction
• B. Anomaly detection
• C. Binary classification
• D. Regression

Answer: D

NEW QUESTION 6
Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming event. Which method should Example Corp use to split the data into a training dataset and evaluation dataset?

• A. Pre-split the data before uploading to Amazon S3
• B. Have Amazon ML split the data randomly.
• C. Have Amazon ML split the data sequentially.
• D. Perform custom cross-validation on the data

Answer: C

NEW QUESTION 7
A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model's accuracy
Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?

• A. Launch multiple training jobs in parallel with different hyperparameters
• B. Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters
• C. Create a hyperparameter tuning job and set the accuracy as an objective metric.
• D. Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter

Answer: B

NEW QUESTION 8
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?

• A. Create a schema in the AWS Glue Data Catalog of the incoming data forma
• B. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
• C. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
• D. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL databas
• E. Have the Analysts query and run dashboards from the RDS database.
• F. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.

Answer: A

NEW QUESTION 9
During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?

• A. The class distribution in the dataset is imbalanced
• B. Dataset shuffling is disabled
• C. The batch size is too big
• D. The learning rate is very high

Answer: D

NEW QUESTION 10
An Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

• A. The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1
• B. The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1
• C. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model
• D. The model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

Answer: C

NEW QUESTION 11
A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions
Here is an example from the dataset
"The quck BROWN FOX jumps over the lazy dog "
Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)

• A. Perform part-of-speech tagging and keep the action verb and the nouns only
• B. Normalize all words by making the sentence lowercase
• C. Remove stop words using an English stopword dictionary.
• D. Correct the typography on "quck" to "quick."
• E. One-hot encode all words in the sentence
• F. Tokenize the sentence into words.

Answer: ABD

NEW QUESTION 12
A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data
Which AWS service should the Data Scientist use?

• A. Amazon Athena
• B. Amazon Redshift Spectrum
• C. AWS Glue
• D. Amazon QuickSight

Answer: A

NEW QUESTION 13
While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?

• A. The model might have prediction errors over a range of target values.
• B. The dataset cannot be accurately represented using the regression model
• C. There are too many variables in the model
• D. The model is predicting its target values perfectly.

Answer: D

NEW QUESTION 14
A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?

• A. Launch the notebook instances in a public subnet and access the data through the public S3 endpoint
• B. Launch the notebook instances in a private subnet and access the data through a NAT gateway
• C. Launch the notebook instances in a public subnet and access the data through a NAT gateway
• D. Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.

Answer: D

NEW QUESTION 15
A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has 15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

• A. An XGBoost model where the objective parameter is set to multi: softmax
• B. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
• C. A regression forest where the number of trees is set equal to the number of product categories
• D. A DeepAR forecasting model based on a recurrent neural network (RNN)

Answer: B

NEW QUESTION 16
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS
Which approach should the Specialist use for training a model using that data?

• A. Write a direct connection to the SQL database within the notebook and pull data in
• B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
• C. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
• D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

Answer: B

NEW QUESTION 17
A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the
target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

• A. Root Mean Square Error (RMSE)
• B. Residual plots
• C. Area under the curve
• D. Confusion matrix

Answer: C

NEW QUESTION 18
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information which model would have the HIGHEST accuracy?

• A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))
• B. Logistic regression
• C. Support vector machine (SVM) with non-linear kernel
• D. Single perceptron with tanh activation function

Answer: B

NEW QUESTION 19
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

• A. Poisson distribution ,
• B. Uniform distribution
• C. Normal distribution
• D. Binomial distribution

Answer: D

NEW QUESTION 20
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

• A. Use an Amazon SageMaker notebook for both feature engineering and model development
• B. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development
• C. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
• D. Use Amazon ML for both feature engineering and model development.

Answer: B

NEW QUESTION 21
A large consumer goods manufacturer has the following products on sale
• 34 different toothpaste variants
• 48 different toothbrush variants
• 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched
Which solution should a Machine Learning Specialist apply?

• A. Train a custom ARIMA model to forecast demand for the new product.
• B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
• C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
• D. Train a custom XGBoost model to forecast demand for the new product

Answer: B

Explanation:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.

NEW QUESTION 22
A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?

• A. Store datasets as files in Amazon S3.
• B. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
• C. Store datasets as tables in a multi-node Amazon Redshift cluster.
• D. Store datasets as global tables in Amazon DynamoDB.

Answer: A

NEW QUESTION 23
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant
Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

• A. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced
• B. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization,and CPU utilization metrics that are outputted by Amazon SageMaker
• C. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker
• D. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.

Answer: B

NEW QUESTION 24
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
• Real-time analytics
• Interactive analytics of historical data
• Clickstream analytics
• Product recommendations
Which services should the Specialist use?

• A. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
• B. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
• C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
• D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

Answer: A

NEW QUESTION 25
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